1. Field of Invention
This invention is directed to analyzing image data to generate continuity data and/or enhancement data.
2. Related Art
Documents scanned at high resolutions typically require very large amounts of storage space. Furthermore, a large volume of image data requires substantially more time and bandwidth to move around, such as over a local or wide area network, over an intranet, an extranet or the Internet, or other distributed networks.
Documents, upon being scanned using a scanner or the like, are typically defined using an RGB color space, e.g., in raw RGB format. However, rather than being stored in this raw scanned RGB format, the document image data is typically subjected to some form of data compression to reduce its volume, thus avoiding the high costs of storing such scanned RGB color space document image data.
Lossless Run-length compression schemes, such as Lempel-Ziv (LZ) or Lempel-Ziv-Welch (LZW), do not perform particularly well on scanned image data or, in general, image data having smoothly varying low-spatial frequencies such as gradients and/or natural pictorial data. In contrast, lossy methods such as JPEG, work fairly well on smoothly varying continuous tone image data. However, lossy methods generally do not work particularly well on binary text and/or line art image data or, in general, on any high spatial frequency image data containing sharp edges or color transitions, for example.
Another type of image compression is shown, for example, in U.S. Pat. No. 6,633,670, which decomposes images into separate layers, each containing a limited number of image element types, e.g., text, line or photographic. Each layer can be compressed separately. Images are decomposed into foreground, background and mask layers. The value of a pixel in the mask layer is determined by partitioning the image into large and small sub-images or blocks. A sub-image mask is created for each sub-image by sorting pixels of the sub-image into large and small sub-images or blocks. A sub-image mask is created for each image by sorting pixels of the sub-image into clusters centered on the luminance of pixels of a pair of pixels or maximum luminance gradient.
One approach to satisfying the compression needs of data, such as the different types of image data described above, is to use an encoder pipeline that uses a mixed raster content (MRC) format to describe the data. The image data, such as for example, image data defining a composite image having text intermingled with color and/or gray-scale information, is segmented into two or more planes. These planes are generally referred to as the background plane and the foreground planes. A selector plane is generated to indicate, for each pixel in the composite image, which of the image planes contains the actual image data that should be used to reconstruct the final output image. Segmenting the image data into planes in this manner tends to improve the overall compression of the image, because the data can be arranged into different planes such that each of the planes are smoother and more readily compressible than is the original image data. Image segmentation also allows different compression methods to be applied to the different planes. Thus, the most appropriate compression technique for the type of data in each plane can be applied to compress the data of that plane.